Ultrasound imaging has become a widely used medical imaging modality, due in part to its effectiveness in safely imaging tissue, its ease of use, and lower cost. Ultrasound has become an essential imaging tool in applications such as identifying tissue anomalies, monitoring fetal development, and assisting in guiding surgical devices in invasive treatments.
More recently, strain imaging has been shown helpful in numerous medical applications, especially in the detection and diagnosis of cancer. In addition, strain imaging may be used for monitoring ablation and deep vein thrombosis. However, generating reliable, high-quality ultrasound strain images from freehand palpations is a challenging task. Strain imaging is highly sensitive to the proper hand motion and the skill of the user.
For example, the best results are achieved when the user compresses and decompresses the tissue uniformly in the axial direction with the proper hand motion. However, it is difficult to control the compression rate as it is governed by hand motion and the frame rate of RF data. Also, small lateral or out-of-plane motions can compromise the quality of images. However, it is difficult to induce pure axial motion with freehand compression.
Sophisticated algorithms have developed to address this problem. However, these algorithms only partially address the problem by compensating for in-plane motions and applying smoothness constraints. The images are also hard to interpret, and artifacts—caused by failure of the strain estimation algorithm or poor hand motion—may be mistaken for lesions inside the soft tissue. As such, there is a need in the art for a strain imaging technique that is not affected by poor hand motion and other sources of signal decorrelation.
To improve the reliability, quality metrics such as persistence in strain images have been developed, as discussed, for example, by JIANG, F. et. al., in their article “A novel image formation method for ultrasonic strain imaging”, Ultrasound Med Biol., April 2007, pp. 643-652, Vol. 33, No. 4, the entire disclosure of which is incorporated by reference herein. This quality indicator is calculated for each image and provided to the user as feedback. Persistence is also used to merge multiple strain images together. To measure the persistence, strain is computed for two pairs of echo frames, and the resulting images are correlated.
Although these techniques offer a major advantage, there remain several limitations. First, the strain has to be estimated before the calculation of the quality metric. With typical ultrasound settings, the frame rate can reach more than 30 Hz. For subsequent frames, an efficient implementation of this image-based metric might cope with this rate. Nonetheless, it is difficult and time consuming to try all combinations in a series of frames. Moreover, the quality metric will not be able to provide feedback to the user regarding whether he/she should adjust the palpation in a certain direction. Also, there is minimal control over the strain.
Accordingly, there is a need in the art for a system and method of improved strain imaging, which does not rely upon complicated algorithms or the experience of the user.